# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Layer that multiplies (element-wise) several inputs."""
# pylint: disable=g-direct-tensorflow-import

from keras.layers.merging.base_merge import _Merge

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.Multiply')
class Multiply(_Merge):
  """Layer that multiplies (element-wise) a list of inputs.

  It takes as input a list of tensors, all of the same shape, and returns
  a single tensor (also of the same shape).

  >>> tf.keras.layers.Multiply()([np.arange(5).reshape(5, 1),
  ...                             np.arange(5, 10).reshape(5, 1)])
  <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[ 0],
       [ 6],
       [14],
       [24],
       [36]])>

  >>> x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
  >>> x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
  >>> multiplied = tf.keras.layers.Multiply()([x1, x2])
  >>> multiplied.shape
  TensorShape([5, 8])
  """

  def _merge_function(self, inputs):
    output = inputs[0]
    for i in range(1, len(inputs)):
      output = output * inputs[i]
    return output


@keras_export('keras.layers.multiply')
def multiply(inputs, **kwargs):
  """Functional interface to the `Multiply` layer.

  Example:

  >>> x1 = np.arange(3.0)
  >>> x2 = np.arange(3.0)
  >>> tf.keras.layers.multiply([x1, x2])
  <tf.Tensor: shape=(3,), dtype=float32, numpy=array([0., 1., 4.], ...)>

  Usage in a functional model:

  >>> input1 = tf.keras.layers.Input(shape=(16,))
  >>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1) #shape=(None, 8)
  >>> input2 = tf.keras.layers.Input(shape=(32,))
  >>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2) #shape=(None, 8)
  >>> out = tf.keras.layers.multiply([x1,x2]) #shape=(None, 8)
  >>> out = tf.keras.layers.Dense(4)(out)
  >>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)

  Args:
      inputs: A list of input tensors (at least 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the element-wise product of the inputs.
  """
  return Multiply(**kwargs)(inputs)
